My backgorund

Why Are We Here Today?

Goals

• Exchange experiences, solutions, and challenges

• Share some of my own lessons from teaching R

• Explore possible approaches for your teaching hurdles

Limitations

• Shaped strongly by my own experiences and by my lab’s context

• Course settings & teaching styles are unique

• First round of this workshop 🎉

Course structure

Challenges in Teaching R

Low student motivation due to

• Lack of intrinsic interest in learning a programming language

• No prior experience with programming

• Unclear understanding of why this is a relevant skill

• Expectation that it will be difficult to learn

Structural challenges

• Students have very heterogeneous background knowledge

• Inconsistent use of statistical software across labs and courses

• Limited time available to teach R

• Large number of students in each course

• Missing resources to provide individual feedback

Getting to know each other

Please share briefly:

• Your name, university, and current role

• What you hope to gain from this workshop

• One challenge (big or small) you or your lab experience when teaching R

Personal lessons learned I

Structure

• Set realistic teaching goals — and stick to them

• Recognize that seminar structure strongly shapes student mindset

• Provide sufficient support: resources, exercises, and constructive feedback

Communication

• Be transparent and straightforward in communication

• You set the tone — be aware of your influence

• When reacting to background issues, explain openly when and why

Personal lessons learned II

Feedback & change:

• Refine the course each semester (step by step)

• Try new approaches; don’t repeat what didn’t work

• Always seek feedback (from the whole class, in presence)

• Feedback can hurt, share your experience with others

• Take feedback seriously, but always consider the context

  • What caused it? What can I do to improve the situation?

• Small adjustments can make a big difference

Teaching R means teaching a language

Programming means failing

“Experienced programmers don’t fail less — they just recover faster.”

• Normalize failure
  • e.g., debug code live, mention common errors
• Encourage self-help strategies first
  • Demonstrate practical debugging techniques
  • How do I read package documentations?

Increasing student motivation

Connect to relevance

• Show examples of what students will be able to do by the end of the course
• Highlight how R is needed for projects, research, and the curriculum
• Show the broader potential of R beyond statistics
• Use diagnostic or test questions to give students with prior knowledge realistic feedback on their level

Make it “fun”

• Playful or surprising examples
  • e.g., art with ggplot, scatterplot dinosaurs
• Interesting themes (even fun column names can help)

Support motivation through feedback

• Timely, constructive, and encouraging feedback

Trade off teaching improvements vs work-load

• little time & resources: start with small improvements

• change the system instead of your workload

  • ask for an student assistance
  • collaborate with other teachers
  • teaching funds
  • new concepts: students feedback the code from each other

• external (free & online) resources:

  • AI, software package, online tutorial, …

R basics

Simulate your own data sets

Example: A quick data set

library(dplyr)
set.seed(42)
n <- 80
df <- tibble(
  subject = 1:n,
  age = sample(18:70, n, replace = TRUE),
  height  = rnorm(n, mean = 170, sd = 10),
  group   = sample(c("control", "treatment"), n, replace = TRUE)
) %>%
  mutate(
    score = rnorm(n, mean = ifelse(group == "treatment", 75, 70), sd = 5),
    response = rnorm(n, mean = 50, sd = 10)
  )

df %>% head()

# A tibble: 6 × 6
  subject   age height group     score response
    <int> <int>  <dbl> <chr>     <dbl>    <dbl>
1       1    66   156. control    73.0     36.2
2       2    54   174. control    77.2     70.5
3       3    18   162. control    65.0     60.2
4       4    42   184. treatment  77.3     49.7
5       5    27   166. control    70.4     57.0
6       6    53   177. control    74.5     40.3

Example: Correlated data

library("MASS")
set.seed(123)

# Define the mean vector
mean_vector <- c(0, 2, 1.8)

# Define the covariance matrix
cov_matrix <- matrix(c(
  1.00, 0.22, 0.10,
  0.22, 1.00, 0.80,
  0.10, 0.80, 1.00
), nrow = 3, byrow = TRUE)

# Generate n correlated random numbers
n <- 200
correlated_data <- mvrnorm(n, mu = mean_vector, Sigma = cov_matrix)

# Convert to a data frame and add column names
correlated_data <- data.frame(correlated_data)
names(correlated_data) <- c("fitness_level", "sport", "health")

# Calculate the correlations
round(cor(correlated_data), 2)

              fitness_level sport health
fitness_level          1.00  0.24   0.08
sport                  0.24  1.00   0.79
health                 0.08  0.79   1.00

Constructive Alignment

Teaching, learning, and assessment should all point in the same direction.

Intended learning outcomes

• What should students be able to do?
• What specific R skills should students be able to demonstrate?
• e.g., import a dataset, run a regression, visualize results

Teaching & learning activities

• How will they practice and engage with the content?
• e.g., exercises at home, debugging code from other students, projects with real data)

Assessment

• How will they demonstrate achievement of the outcomes?
• e.g., open book exams, oral explanations, …

Constructive Alignment – Background

• Developed by John Biggs (1996)
• Based on the idea that students construct their own learning
• The role of the teacher: design learning environments that support this process
• Alignment means:
  • Learning outcomes define what students should achieve
  • Teaching activities give opportunities to practice
  • Assessment measures achievement of outcomes

Alternative exams

Flipped Classroom – The Concept

Flipped Classroom – Challenges

Students may be unprepared in class

High effort for instructors:

• Preparing quality pre-class materials (texts, videos, exercises)
• Designing effective in-class activities can be difficult

Requires a cultural shift:

• Students expect “being taught” in class
• Teachers must let go of content delivery
• Teachers must be “strict”

Teaching materials

ChatGPT — students

ChatGPT — teachers

• Efficiency first
  • save time for what matters most (teaching & feedback)
• Friendlier, more constructive feedback phrasing
• More content
  • Generate exercises, code examples, explanations
• Alternative explanations (multiple ways to explain)
• Adapt exercises to different levels (beginner vs advanced)
• …

Concept

Pros & cons

Default design

Simple examples I

Tutorial on quizzes

question_radio(
  "Is this a good question?",
  answer("yes", correct = TRUE),
  answer("no", message = 'This is a good question.')
)

Simple examples II

question_numeric(
 "What is pi rounded to 2 digits?",
 answer(3, message = "Don't forget to use the digits argument"),
 answer(3.14, correct = TRUE),
 allow_retry = TRUE,
 min = 3,
 max = 4,
 step = 0.01
)

Simple examples III

learnr::question_checkbox(
  "Select all the toppings that belong on a Margherita Pizza:",
  answer("tomato", correct = TRUE),
  answer("mozzarella", correct = TRUE),
  answer("basil", correct = TRUE),
  answer("extra virgin olive oil", correct = TRUE),
  answer("pepperoni", message = "Great topping! ... just not on a Margherita Pizza"),
  answer("onions"),
  answer("bacon"),
  answer("spinach"),
  random_answer_order = TRUE,
  allow_retry = TRUE,
  try_again = "Be sure to select all toppings!"
)

Otter as an example

Visit the Otter site

Contact